PROJECT SUMMARY Cervical spinal cord injury results in the loss of arm and hand function, which significantly limits independence and results in costs over the person?s lifespan. A brain-computer interface (BCI) can be used to bypass the injured tissue to enable control of a robotic arm and to provide somatosensory feedback. Two primary limitations of current state-of-the-art BCIs for arm and hand control are: (1) the inability to control the forces exerted by the prosthetic hand and (2) the lack of somatosensory feedback from the hand. In the proposed study, we seek to considerably improve dexterous control of prosthetic limbs by implementing decoding strategies that enable the user to not only control the movements of the arm and hand, but also the forces transmitted through the hand. We anticipate that our biomimetic approach to decoding will yield intuitive, dexterous control of the prosthetic hand. Tactile sensations will be conveyed to the user through intracortical microstimulation (ICMS) of somatosensory cortex. The spatiotemporal patterns of stimulation will be based on our basic scientific understanding of how tactile information is encoded in somatosensory cortex, which we expect will result in more natural and intuitive sensations. In order to achieve our goal of developing a dexterous neuroprosthesis, we have brought together a team with human BCI experience from the University of Pittsburgh along with the basic science expertise at both Pitt and the University of Chicago. We will collaborate with experts in implantable neurotechnology (Blackrock Microsystems) and robotics (The Biorobotics Institute) to ensure that the device hardware allows us to take a biomimetic approach for control and feedback with an eye toward clinical translation. A total of 4 participants will be tested in a multisite study to accomplish the following three specific aims. Aim 1: Evoke natural and intuitive tactile sensations through ICMS of somatosensory cortex. We expect that biomimetic ICMS will evoke sensations that more closely resemble everyday tactile sensations and intuitively convey information about contacted objects than does standard fixed-frequency ICMS. Aim 2: Derive kinematic and kinetic signals from motor cortex for hand control. We will assess the degree to which motor cortical neurons encode forces exerted on objects. Based on these observations, we will develop hybrid decoders that enable controlling both the movement and force using a synergy-based approach. Aim 3: Demonstrate improved arm and hand function with a biomimetic sensorimotor BCI that combines the sensory feedback developed in Aim 1 with the hybrid decoding developed in Aim 2. A battery of functional assessments will be used including novel metrics designed specifically for sensorimotor prosthetics along with well-established tests identified in the NIH Common Data Elements. We anticipate that subjects will substantially improve their dexterity using a biomimetic BCI as compared to non-biomimetic BCIs or BCIs without somatosensory feedback.